Create chain_problems.py

chain_problems.py

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+# chain_problems.py
+import json
+import logging
+from typing import Dict
+from langchain import PromptTemplate, LLMChain
+from models import chat_model
+
+logger = logging.getLogger(__name__)
+
+problem_prompt_template = PromptTemplate(
+    input_variables=["responses", "internal_report"],
+    template=(
+        "You are a wellness analyst. You have the following user responses to health-related questions:\n"
+        "{responses}\n\n"
+        "You also have an internal analysis report:\n"
+        "{internal_report}\n\n"
+        "From these inputs, determine a 'problem severity percentage' for the user in the following areas: "
+        "sleep, exercise, stress, and diet. "
+        "Return your answer in JSON format with keys: sleep_problem, exercise_problem, stress_problem, diet_problem.\n"
+        "Ensure severity percentages are numbers from 0 to 100.\n\n"
+        "JSON Output:"
+    )
+)
+problem_chain = LLMChain(llm=chat_model, prompt=problem_prompt_template)
+
+def analyze_problems_with_chain(responses: Dict[str, str], internal_report: str) -> Dict[str, float]:
+    responses_str = "\n".join(f"{q}: {a}" for q, a in responses.items())
+    raw_text = problem_chain.run(responses=responses_str, internal_report=internal_report)
+    try:
+        start_idx = raw_text.find('{')
+        end_idx = raw_text.rfind('}') + 1
+        json_str = raw_text[start_idx:end_idx]
+        problems = json.loads(json_str)
+        for key in ["sleep_problem", "exercise_problem", "stress_problem", "diet_problem"]:
+            problems.setdefault(key, 0.0)
+        return {k: float(v) for k, v in problems.items()}
+    except Exception as e:
+        logger.error(f"Error parsing problem percentages from LLM: {e}")
+        return {
+            "sleep_problem": 0.0,
+            "exercise_problem": 0.0,
+            "stress_problem": 0.0,
+            "diet_problem": 0.0
+        }